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Author:

Zan, Tao (Zan, Tao.) | Wang, Min (Wang, Min.) (Scholars:王民) | Cui, Xiao-Guang (Cui, Xiao-Guang.) | Fei, Ren-Yuan (Fei, Ren-Yuan.)

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EI Scopus PKU CSCD

Abstract:

In order to improve the ink-presetting precision in the different printing parameters and printing environment, in this paper a novel approach using ANN technique is presented. For precision and rapid ink-presetting in the digital printing process, with the actual qualified printing products as training samples, the printing parameters and dot area percentage as input characteristic values and actual ink key values as ANN output, a three-layer BP network is selected to establish the mapping between the graph-text information and the values of the ink key. The experiment result shows that this method can shorten the adjusting time of the offset press effectively, improve the printing efficiency and reduce production cost.

Keyword:

Presses (machine tools) Offset printing Neural networks Digital printing Network layers

Author Community:

  • [ 1 ] [Zan, Tao]The Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Wang, Min]The Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Cui, Xiao-Guang]The Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Fei, Ren-Yuan]The Key Laboratory of Beijing Municipality on Advanced Manufacturing Technology, College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2011

Issue: 5

Volume: 37

Page: 657-660

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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